Heart rate variability in patients with cardiovascular diseases

Abstract

Heart rate variability (HRV) has been reported to predict overall mortality and the risk of cardiovascular disease events in patients, including those with heart failure. However, inconsistent methods of recording and analyzing HRV parameters, along with a lack of randomized data substantiating its clinical efficacy and potential to guide treatment decisions for improved patient outcomes, have limited its use in clinical settings. With the advancements in technologies such as artificial intelligence and machine learning, and emergence of ablation procedures that can alter autonomic function, this article re-explores HRV assessment methods, their potential for clinical application, the issues encountered in using them in clinical research, and potential approaches to studying HRV in the future (Graphical Abstract).